A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Parameters for High-Frequency Environmental Data Article Swipe

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Shirley Rojas‐Salazar , Erin M. Schliep , Christopher K. Wikle , Emily H. Stanley , Stephen R. Carpenter , Noah R. Lottig ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2109.09949

Environmental time series data observed at high frequencies can be studied with approaches such as hidden Markov and semi-Markov models (HMM and HSMM). HSMMs extend the HMM by explicitly modeling the time spent in each state. In a discrete-time HSMM, the duration in each state can be modeled with a zero-truncated Poisson distribution, where the duration parameter may be state-specific but constant in time. We extend the HSMM by allowing the state-specific duration parameters to vary in time and model them as a function of known covariates observed over a period of time leading up to a state transition. In addition, we propose a data subsampling approach given that high-frequency data can violate the conditional independence assumption of the HSMM. We apply the model to high-frequency data collected by an instrumented buoy in Lake Mendota. We model the phycocyanin concentration, which is used in aquatic systems to estimate the relative abundance of blue-green algae, and identify important time-varying effects associated with the duration in each state.

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Hidden semi-Markov model Covariate Hidden Markov model Duration (music) Statistics Poisson distribution Econometrics Count data Conditional independence State (computer science) Mathematics Markov chain Markov model Computer science Artificial intelligence Algorithm Markov property Physics Acoustics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2109.09949
PDF: https://arxiv.org/pdf/2109.09949
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References: 25
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DOI: https://doi.org/10.48550/arxiv.2109.09949

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Title: A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Parameters for High-Frequency Environmental Data

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Publication year: 2021

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Publication date: 2021-09-21

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Authors: Shirley Rojas‐Salazar, Erin M. Schliep, Christopher K. Wikle, Emily H. Stanley, Stephen R. Carpenter, Noah R. Lottig

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Landing page: https://arxiv.org/abs/2109.09949

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Concepts: Hidden semi-Markov model, Covariate, Hidden Markov model, Duration (music), Statistics, Poisson distribution, Econometrics, Count data, Conditional independence, State (computer science), Mathematics, Markov chain, Markov model, Computer science, Artificial intelligence, Algorithm, Markov property, Physics, Acoustics

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abstract_inverted_index.relative	149
abstract_inverted_index.abundance	150
abstract_inverted_index.addition,	100
abstract_inverted_index.collected	127
abstract_inverted_index.important	156
abstract_inverted_index.parameter	56
abstract_inverted_index.approaches	12
abstract_inverted_index.associated	159
abstract_inverted_index.assumption	116
abstract_inverted_index.blue-green	152
abstract_inverted_index.covariates	86
abstract_inverted_index.explicitly	28
abstract_inverted_index.parameters	73
abstract_inverted_index.conditional	114
abstract_inverted_index.frequencies	7
abstract_inverted_index.phycocyanin	138
abstract_inverted_index.semi-Markov	18
abstract_inverted_index.subsampling	105
abstract_inverted_index.transition.	98
abstract_inverted_index.independence	115
abstract_inverted_index.instrumented	130
abstract_inverted_index.time-varying	157
abstract_inverted_index.Environmental	0
abstract_inverted_index.discrete-time	38
abstract_inverted_index.distribution,	52
abstract_inverted_index.concentration,	139
abstract_inverted_index.high-frequency	109, 125
abstract_inverted_index.state-specific	59, 71
abstract_inverted_index.zero-truncated	50
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
sustainable_development_goals[0].id	https://metadata.un.org/sdg/14
sustainable_development_goals[0].score	0.46000000834465027
sustainable_development_goals[0].display_name	Life below water
citation_normalized_percentile